Battery and workstation monitoring system and display

ABSTRACT

An Asset Management System and a method for managing a fleet of assets is provided. The system is capable of determining use states and high-use periods of a fleet of mobile workstations. Use states are determined by sensors resident on mobile workstations, the sensors operable to detect the occurrence of a specified event. The Asset Management System is able to interpret data sent by the sensors and determine a type of use and use state for each mobile workstation based on the data or lack of data sent by the sensors. The Asset Management System is operable to determine periods of high-use across the fleet of mobile workstations. The Asset Management System is also operable to determine a return-on-investment level of each mobile workstation in the fleet and generate a heat map based on those levels.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent No.62/543,549 filed Aug. 10, 2017 entitled BATTERY AND WORKSTATIONMONITORING SYSTEM AND DISPLAY and U.S. Provisional Patent No.62/628,623, filed Feb. 9, 2018 entitled BATTERY AND WORKSTATIONMONITORING SYSTEM AND DISPLAY, which are herein incorporated byreference in their entireties.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the reproduction of the patent document or the patentdisclosure, as it appears in the U.S. Patent and Trademark Office patentfile or records, but otherwise reserves all copyright rights whatsoever.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

REFERENCE TO SEQUENCE LISTING OR COMPUTER PROGRAM LISTING APPENDIX

Not Applicable

BACKGROUND

The present subject matter relates generally to asset management andmore particularly to the monitoring of a fleet of mobile workstations,battery use and health, and providing an effective user interface fortracking the use and health of the fleet.

In the current state of the art, hospitals and other health careproviders use a variety of different assets to streamline and supplementthe care that they provide to patients. However, the number of devicesand the use of those devices is inherently difficult to track, includingreplacement dates and allocation across a facility. Devices may bespread and moved across a large facility, making it difficult to tracklocation and keep an accurate inventory of the devices. Furthermore, thedevice management may be difficult as certain devices may need to bedecommissioned or replaced after a certain time period.

Battery health representations include tabular displays of batterycharge or battery health. Moreover, traditional battery capacitymeasurements calculate only the charge capacity of the initial charge,and do not adapt to subsequent usage or charging patterns. Moreparticularly, a battery will have a different charge capacity during itsfirst use and several subsequent uses as the charge capacity decreasesover time. The battery is generally still healthy during the firstseveral uses even though the charge capacity is decreasing. Manybatteries are measured for health in comparison to the initial chargecapacity.

Another inherent difficulty includes allocating the resources to theright people and departments without having unused devices or adeficiency of those devices in the facility. Use of medical carts orsimilar devices in hospitals is not distributed evenly across the24-hour day, rendering traditional (average) measures of useineffective. Many of these devices are critical to patient care, andmust be available and locatable at any time.

Additionally, battery-powered devices in hospital settings frequentlyresult in multiple failures or can repeatedly fail during use. Thesefailures generally go unreported by clinicians, creating a backlog ofnon-functional units until there are sufficient failures within thefleet of devices as to negatively impact patient care.

Further problems in the present state of the art exist in the statedfull-charge capacity of the battery. Specifically, present measurementsof “full-charge” capacities are static, such that the capacity of abattery is rated at point-of-manufacture, point-of-sale, orpoint-of-first-use, and is not subsequently updated despite usage of thebattery.

Previous attempts to monitor battery health and predict future batteryoutcomes have relied upon limited amounts of data (often taken from thebattery's initial stages) or rely on direct measurements, such asvoltage or temperatures outside of a predetermined range orspecification. However, no solutions currently exist which base need forbattery service on the use patterns of a fleet of devices to determinewhen a user can or should switch to another similar device.

Accordingly, there exists a need for a battery and workstationmonitoring system which can manage, predict, and display the assethealth of a plurality of devices within and across a fleet of similardevices.

BRIEF SUMMARY

The present disclosure generally provides systems and associated methodsfor managing a fleet of mobile workstations, a fleet of batteries, andthe use and health of the workstations and batteries.

In some embodiments, the present disclosure provides a system formonitoring and tracking battery health relative to the capacity of abattery in charging and discharging. A charge sensor is in electricalcommunication with the battery such that the sensor detects the level ofcharge of a battery. The sensor generates a signal when a battery hasdischarged all of its power and generates a signal when it has reachedmaximum capacity during charging. The signal is sent to a transmitterwhich transmits a second signal to a processor, wherein the processorcompares the discharge and full capacity charge data to averagedischarge and full capacity charge data and outputs a health of thebattery. The processor then aggregates the health of a fleet ofbatteries and sends the data to a display.

In further embodiments, the present disclosure provides a system formonitoring usage and return on investment for a fleet of mobileworkstations. The system comprises sensors for detecting use of a mobileworkstation. The sensor is operable to measure energy output frombatteries electrically coupled to the mobile work station. When apredetermined threshold of energy output is measured from the batteries,the mobile workstation is in use and the sensor generates an “in use”signal. A second sensor may be used for determining a user's proximityto the mobile work station. The second sensor detects when a user islocated proximate the mobile work station, the sensor generates an “atthe ready” signal. A third sensor may be used to detect when a mobileworkstation is in transit. The third sensor may detect acceleration,location, or vibration. When a threshold input is reached in the thirdsensor, the sensor generates an “in transit” signal. The first, second,and third sensors may be electrically coupled to a transmitter whichtransmits the signals to a processor. The processor may receive thesignals and aggregate the activity status of a fleet of mobileworkstations. The processor may then determine periods of high activityand low activity. The processor may then compare the usage of eachmobile workstation during periods of high activity to determine a returnon investment (ROI) value for each mobile workstation. The processor maythen generate a display signal which is transmitted to a display.

Additional objects, advantages, and novel features will be set forth inpart in the description which follows, and in part will become apparentto those skilled in the art upon examination of the followingdescription and the accompanying drawings or may be learned byproduction or operation of the examples. The objects and advantages ofthe concepts may be realized and attained by means of the methodologies,instrumentalities, and combinations particularly pointed out in theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary embodiment of an asset management system.

FIG. 2 is an exemplary embodiment of a heat map displaying the chargelevel of a fleet of assets.

FIG. 3 is an exemplary embodiment of an asset management system using abeacon for real-time location services.

FIG. 4 is an exemplary embodiment of a battery inventory on a graphicaluser interface.

FIG. 5 is an exemplary embodiment of a workstation inventory on agraphical user interface.

FIG. 6 is an exemplary embodiment of a heat map displaying the batteryhealth level of a fleet of assets.

FIG. 7 is an exemplary embodiment of a predictive calendar for predictedhealth levels of a fleet of batteries.

FIG. 8 is an exemplary embodiment of an asset management system whereina battery communicates with the network via a second asset.

FIG. 9 is an exemplary embodiment of an asset authenticating a user andsetting a use state for a device based on proximity of a user.

FIG. 10 is an exemplary embodiment of a column bar chart usage of afleet of assets broken down into hourly usage.

FIG. 11 is an exemplary embodiment of a heat map of the return oninvestment of a fleet of workstations.

FIG. 12 is an exemplary embodiment of a column bar chart of the returnon investment of a fleet of workstations.

FIG. 13 is an exemplary embodiment of a homepage of a graphical userinterface.

FIG. 14 is an exemplary embodiment of a dashboard view on a graphicaluser interface.

FIG. 15 is an exemplary embodiment of a workstation utilization view ona graphical user interface.

FIG. 16 is an exemplary embodiment of a inefficiency chart view on agraphical user interface.

FIG. 17 is an exemplary embodiment of an asset drift report view on agraphical user interface.

FIG. 18 is an exemplary embodiment of a method for determining andpredicting battery health.

FIG. 19 is an exemplary embodiment of a method for determining usestates and return on investment of an asset.

FIG. 20 is a first exemplary embodiment of an asset management systemhaving assets with varying sensor arrays resident on assets.

FIG. 21 is a second exemplary embodiment of an asset management systemhaving assets with varying sensor arrays resident on assets.

FIG. 22 is a third exemplary embodiment of an asset management systemhaving assets with varying sensor arrays resident on assets.

DETAILED DESCRIPTION

While the making and using of various embodiments of the presentinvention are discussed in detail below, it should be appreciated thatthe present invention provides many applicable inventive concepts thatare embodied in a wide variety of specific contexts. The specificembodiments discussed herein are merely illustrative of specific ways tomake and use the invention and do not delimit the scope of theinvention. Those of ordinary skill in the art will recognize numerousequivalents to the specific apparatus and methods described herein. Suchequivalents are considered to be within the scope of this invention andare covered by the claims.

In the drawings, not all reference numbers are included in each drawing,for the sake of clarity. In addition, positional terms such as “upper,”“lower,” “side,” “top,” “bottom,” etc. refer to the apparatus when inthe orientation shown in the drawing, or as otherwise described. Aperson of skill in the art will recognize that the apparatus can assumedifferent orientations when in use.

To meet the needs described above and others, the present disclosureprovides a battery and workplace monitoring system which can represent,manage, predict, and display the battery health of a plurality ofdevices within and across a fleet of similar devices or assets.

The battery and workstation monitoring system and display comprisesvarious parts and features for allowing users to manage and track mobileworkstations and batteries individually and as a fleet. A first aspectof the disclosure is a system for providing a heat map including chargelevels of a fleet of batteries, health levels of a fleet of batteries,and a return on investment for a fleet of workstations. A second aspectof the disclosure is a Battery Predictive Report providing predicteddates when assets will fall below predetermined health thresholds. Athird aspect of the disclosure is a Return on Investment reportincluding fleet-wide and individual asset return of investment for afleet of assets base on asset usage and peak usage intervals. A fourthaspect of the disclosure includes a Graphical User Interface fordynamically monitoring and managing a fleet of assets.

Heat Map

A first aspect of the disclosure provides an Asset Management System(AMS) 10 which provides a heat map format for monitoring and managing afleet of assets. Referring to FIG. 1, the AMS 10 may comprise a remoteserver 20 operably linked to an asset 11 or a fleet 13 of assets via anetwork 15. The assets 11 may comprise a variety of devices includingbatteries, mobile workstations, medical equipment, mobile computers,tablets, phones, diagnostic equipment, and any other devices known toone of skill in the art.

A heat map 16 is a method for visually displaying data on a graphicaluser interface 32 for quick consumption and understanding. In someembodiments, a heat map 16 may display a series of symbols 18 in theform of rectangles, color-coded to reflect traits of a single asset 11or a fleet 13 of assets. The information may be represented in manydifferent forms including displaying the data by representing the healthlevel of each of the batteries by shape, color, shading, density ofmarkings, or any other method readily available and known to one ofskill in the art. The heat map 16 may also be scalable and may have theability to select different ranges of the fleet 13 of assets.

Each heat map 16 is populated with information based on specific inputsproduced by the AMS 10 and received and processed at the remote server20. Each heat map 16 may display specific dynamic outputs produced bythe AMS 10, including charge levels of batteries in a fleet 13, currenthealth levels of batteries in a fleet 13, or Return on Investment of afleet 13 of assets.

Each battery may be represented by a single representation 18. In otherembodiments, sub-groups of the fleet 13 may be represented by a singlerepresentation 18. In one embodiment, the fleet 13 of batteries (totalfleet, or partial subset fleet based on use areas) may be monitored forbattery health where the relative health information is presented in aheat map 16 format on a graphical user interface 32 or display.

The heat map 16 may include additional functionalities beyond justdisplaying information for consumption by a user. Many of thesefunctionalities will be described in more detail with specific referenceto applications and in specific examples, however, the disclosure ofthose functionalities is to be broadly applied to each variation of theheat map described herein or which would be known to one of skill in theart. It is to be understood that the following sections incorporate thedisclosure of a heat map are to be applied specifically in the contextof battery charge, battery health and predictive health, and workstationfleet Return on Investment.

Accurate Battery Charge Representation and Location

A second aspect of the disclosure describes a system and method ofbattery charge determination and representation. One embodimentdescribes a system and method of battery charge representation by theAMS 10. Battery charge may be displayed in a heat map format such that afleet 13 of batteries may all be represented on the heat map 16 and theoverall charge level and need for charging of an entire fleet may bequickly understood.

Referring to FIG. 2, a heat map 16 may be implemented to display thecharge levels of a fleet 13 of batteries (as a metric of the percent ofcurrent charge relative to the potential full charge or the most recentfull charge capacity), wherein the heat map 16 provides alternativefunctionalities for managing the fleet 13 which are described herein.

A user may ascertain a variety of data relating to specific batteriesand more broadly to specific assets 11 that might be represented on aheat map 16. The symbol 18 on the heat map 16 which represents aspecific asset 11 may be selected to generate information relating tothat asset, including location information. A Real-Time Location System(RTLS) 30 will presently be described and may be applied to each portionof this disclosure as will be described throughout.

The RTLS 30 may be integrated into the AMS 10 and provides a system andmethod for tracking assets 11 across a fleet 13 of assets. Referring toFIG. 3, a first embodiment includes assets 11 and a fleet 13 of assetssending and receiving signals 22 to beacons 21 which relay informationto the server 20 through a network 15. In some embodiments locationdevices 12 are deployed on each asset 11 and are operable to detect asignal 22 from the beacons 21. In some embodiments, the location device12 may be operable to detect a signal 22 from another location device 12or a beacon 21. The location device 12 is capable of determining alocation within an area based on location principles such astriangulation based on the network of various location devices andbeacons within the area. The beacons 21 are stationary and areconfigured to provide a static location for determining the location ofassets 11 by the location devices 12 within a building, room, or area.The location of a first location device 12 may be determined by the RTLS30 and may act as a temporary or ancillary beacon when the AMS 10 isunable to provide an accurate location of a second location device basedon the signals sent and received from the stationary beacons 21.Location devices 12 may be operably coupled to an asset 11 or integratedinto the circuitry of an asset 11.

Each location device 12 is associated with a specific asset 11. In oneexample, the location device 12 may be associated with a battery, aworkstation, or any other device to be tracked within an area. Eachasset 11 of the fleet 13 is assigned an asset profile 28 which is savedon a computer memory storage device of the AMS 10, which could be on aserver 20. Each asset 11 of the fleet 13 of assets is assigned an assetlocation based on asset location data 26 associated with each asset 11.The asset location data 26 is associated with the asset profile 28. Theasset profile 28 is updated periodically to reflect the most currentlocation data associated with the asset. The updating of the assetprofile 28 and the asset location data 26 of the asset 11 provides adynamic view or snapshot of the location of a fleet 13 of assets withina system.

In some embodiments, the RTLS 30 may not be integrated in to the AMS 10,and the RTLS 30 may communicate the data to the AMS 10 via a network 15.The RTLS 30 may store a location device profile for each location device12, and the AMS 10 may associate the data received for each locationdevice 12 with the asset profile 28 to which the location device 12 isassigned. The RTLS 30 may specifically include battery location data andworkstation location data.

Other data may be associated with each asset profile 28. In one example,a battery profile 28 on the server 20 may include charge level data,asset health data 29, asset usage data (or use state data) 27,manufacturer warranty data, any other information suitable and known toone of skill in the art. The AMS 10 is able to provide recommendationsto users based on the information contained in the data of each of theasset profiles 28. In one example, if an asset 11 is a battery on amobile workstation in a hospital and the battery is running low oncharge, the asset management system 20 is capable of making arecommendation for locating a replacement battery nearby based onlocation data and charge level data of batteries in the AMS 10. The AMS10 is able to determine the closest fully-charged or almostfully-charged batteries. The AMS 10 may be capable of filtering outthose batteries that are in use on a different asset, for example,another mobile workstation based on location data, charge data, etc. Inanother example, the AMS 10 may recommend to a user where the nearestcharging station or available charging terminal is located to place thedischarged battery.

Because the AMS 10 is continuously updating the data associated witheach asset profile 28, the AMS 10 is capable of providing dynamicrecommendations for use of assets 11. For example, a user may be alertedthat the charge level of a battery is running low while in a first roomwith a patient. The nearest charging station at that moment may be at anurse station across the hall from the first room. The AMS 10 mayrecommend that a fully-charged battery be taken from the chargingstation at the nurse station and the discharged battery be placed on thesame battery station for charging. However, the user may not be able totake the battery immediately to the charging station at the nursestation before visiting another patient on a different floor. Once thelocation data 32 associated with the battery is updated in the batteryprofile 28, the AMS 10 locates a second charging station which has anavailable fully-charged battery closest to the updated location of themobile workstation. The AMS 10 then updates the recommendation to theuser based on the new location data.

The AMS 10 is also capable of providing reports of each asset 11 of thefleet 13 of assets. FIG. 4 and FIG. 5 demonstrate exemplary embodimentsof workstation and battery inventories produced by the AMS 10. Thisincludes an assigned location 38 of the asset 11 and the actual location40 of the asset 11. For example, because mobile workstations areinherently moveable between locations, it may be difficult to track andkeep an accurate inventory of the mobile workstations, even whendesignated for specific use in a specific location. In one example, amobile workstation may be assigned specifically to a floor which housesan intensive care unit and a burn unit. A provider may be moving apatient from the intensive care unit to a transitional care unit on adifferent floor. The provider might take a cart that is technicallyassigned to the burn unit while seeing the patient in the intensive careunit and move the cart to the transitional care unit while transferringthe patient. A user in the burn unit is able to access via a computerterminal the system to view the location of the mobile workstation thathas been moved from the burn unit. A second user in the transitionalcare unit may notice that there is an excess number of carts in theirunit and may access the system via a second computer terminal todetermine the carts' locations and assigned locations. Thus each unit ordepartment is able to maintain control of and access to inventory. Thedynamic updating of location improves the system by providing users withthe ability to quickly locate, return, or reallocate assets, andspecifically inherently mobile assets, to the appropriate locationwithin an area.

In some embodiments the real-time location system 30 and the othersystems associated with this disclosure are a cloud-based system thatmonitors and manages mobile workstations and mobile assets withreal-time visibility. In other embodiments, the system may be operableover a local network and server.

Current Battery Health Representation

A third aspect of the disclosure describes a system and method ofvariable battery health representation by the AMS 10. Asset health maybe determined in a variety of situations specific to the asset 11 whichis being managed. Generally, an AMS 10 requires a variety of sensorsoperably coupled to an asset 11 or a fleet 13 of assets for monitoring anumber of factors indicative of health, such as temperature, flexion,tension, charge, etc. Asset health may be displayed in a heat map 16 andmay be used in a variety of other interfaces and applications outside ofa heat map format.

Battery health may be determined as a ratio of a Manufactured FullCharge Capacity (MFCC) and the currently available Full Charge Capacity(FCC). Specifically, the MFCC is the FCC of a battery at the time ofmanufacture. The ratio of the currently available FCC of a batterydivided by the MFCC of the battery when the battery was firstmanufactured or new. The MFCC can be a set numeric value assigned by themanufacturer at the time of manufacture specifically. Each battery maybe individually assessed to determine the specific battery's MFCC or themanufacturer may assign each battery with a MFCC based on the average ofall similar battery models.

Alternatively, battery health may be determined as a ratio of thecurrently available Full Charge Capacity (FCC) divided by the AdjustedFull Charge Capacity (AFCC) of a battery. Separately, battery chargelevels may be determined using similar factors, specifically, thebattery charge level may be the ratio of the current charge levelcompared to a recent FCC, the MFCC, or the AFCC.

In one example, with a fleet of batteries, sensors 24 may be operablycoupled to each battery and configured to sense when a battery hasreached a FCC. A memory storage device may retain the FCC of thebatteries so an asset processor 25 is able to retrieve that informationto make a variety of determinations based on the previous FCC's of abattery. In another embodiment, the AMS 10 may store the FCC's over aninitial period and the asset processor 25 may take the FCC's over theinitial period to calculate an AFCC. The AFCC is the average maximumcharge a battery will hold during its first 30 days use. This differsfrom the initial measurement of a brand-new battery, as this valuevaries during the first several charges, and the battery monitor systemreports values that shift based on the initial charge levels and chargepatterns. In other embodiments, the FCC and AFCC are calculated andstored on the remote server 20 and by the processor 14 of the remoteserver 20.

In one example, a user interface 32 may display battery health relatingto each battery of a fleet of batteries. The user interface may includea heat map of the all of the batteries that have been profiled in thesystem, as shown in FIG. 6. A user may then select, via the userinterface 32, a representation 18 of the battery which is displayed onthe user interface. Once the selection has been made, in someembodiments, both the location data, any other data generated by sensorscoupled to the assets, and any other data relating to the selectedbattery may be displayed on the user interface

In one embodiment, battery health can be more accurately predicted byusing the AFCC. AFCC provides a more accurate representation of abattery's charge capacity, because of the normal fluctuations in the FCCof a healthy battery during the initial uses of the battery. In someembodiments, the AFCC is the average charge a battery will hold duringits first thirty (30) days of use. In other embodiments, the AFCC is theaverage charge a battery will hold over a variety of periods of time,including 1 week, 2 weeks, 3 weeks, 2 months, or any other acceptableperiod of time as may better predict a battery's FCC dependent on thespecific properties of the given battery as one of ordinary skill in theart might determine. A battery charge capacity sensor is electricallycoupled to the battery to measure the charge capacity of the battery.During the first thirty (30) days of use of the battery, the batterycharge capacity sensor will determine the FCC of the battery when thebattery is fully charged. The battery charge capacity sensor willgenerate a FCC signal containing the FCC data of the battery. Aprocessor 14 will then receive the FCC data. In some embodiments, theprocessor 14 will then send the FCC data to a memory storage device. Thestorage device will collect the FCC data of a battery for the firstthirty (30) days of the battery's use. The processor 14 will thenretrieve the battery's FCC data and create an average of the FCC data,the average is then assigned to the battery as an AFCC for the battery.The battery's health is then determined by the ratio of the battery'scurrent or most recent FCC to the AFCC that has been assigned to thatbattery. In some embodiments, the AFCC may be determined by determiningthe average FCC for a group of batteries over the first thirty (30) daysof each battery's life. In other embodiments, the AFCC is determined bycalculating the average FCC a fleet of batteries or subset of the fleetover the first thirty (30) days of each batteries life.

Referring again to FIG. 6, a heat map 16 may depict one aspect of thepresent disclosure. Specifically, FIG. 6 presents a means of variablebattery health representation. Battery health is determined as a ratioof the currently available FCC divided by the MFCC or AFCC of a battery.As can be seen in FIG. 18, variable health representation may include“Detect Full Charge Capacity (FCC) of a Battery” 181, “Store FCC Dataover a Period of Time” 182, “Determine Average/Manufacturer Full ChargeCapacity (A/MFCC)” 183, “Determine Battery Health Level based on CurrentFCC relative to A/MFCC” 184, and “Generate Heat Map of Battery HealthLevels for Fleet of Batteries” 185.

In an embodiment, the heat map 16 represents each battery within a fleet13, with the current battery health expressed in a color, from green(best) to red (worst). Additionally, the heat map 16 includes a scrolland scale control to expand and collapse the map, or scroll to one sideor the other, in order to view either the entire fleet 13 or pan andzoom to any portion of the heat map 16.

In one embodiment, a user may select a setting in which the batteryhealth data may be displayed such that each symbol 18 representing abattery is arranged into a section according to its health relative tothe other batteries in the fleet 13. This allows a user to quicklycomprehend the overall health of the fleet 13. The user may furtherrefine the data that is being displayed by selecting the battery bydepartment. In one example, a hospital has many departments wherebatteries and mobile workstations are used including emergencydepartments, intensive care units, surgical centers, laboratories,cardiology departments, neurology departments, and all throughout ahospital including nursing stations, patient rooms, offices, andoperating rooms. In one embodiment, each battery may be assigned to aspecific department. In another embodiment, each battery may be trackedby location and be assigned to a department based on its location. Thisinformation and the assignments may be represented by data associatedwith the profile 28 of an asset 11 on the memory storage device of theAMS 10. As such, a user may select a setting on the AMS 10 to displaythe battery health based on the department to which it has been assignedor based on the location of the battery. Department designations may beused in any setting including warehouses, businesses, malls, etc.

In some embodiments, the heat map 16 may be interactive. In oneembodiment, the heat map 16 may be scalable such that varying levels ofdata may be displayed on a single map. This allows a user to select amore detailed view of the battery health or a broader picture of thehealth of the fleet 13. In other embodiments, each symbol 18representing the life of the battery may be selectable such that whenthe symbol 18 is selected, information regarding the specific batterythat is represented by the symbol 18 is displayed. In one example, whena specific symbol 18 is selected, the AMS 10 may display the identity,location, length of life, make and model, last use, serial number,charge level, estimated run time, cycle count, warranty information, orany other information that is readily ascertainable by one of skill inthe art.

In some embodiments, a FCC can be established for each battery. Thetarget FCC may differ from the published FCC. For example, the chargethat each battery will accept over its initial weeks of use is measuredas well as the lowest charge level to which it can be discharged toestablish a target FCC, which may differ from the published FCC. Thisestablishes the target (or new battery) FCC for each individual battery,and the target FCC data may be stored on the memory storage device ofthe AMS 10 and associated with the profile 28 of that battery.

In other embodiments, the range over which the battery can be charged iscontinually monitored, wherein the current available energy that can beaccessed between the current FCC and the level to which it can bedischarged is measured. The ratio of current energy available or FCCdivided by the “new battery” charge capacity available or target FCC isdetermined for each battery and that ratio, expressed as a percentage,is the battery health. Assets 11 can be filtered by department to see asubset of the data. Battery health data may be stored on the memorystorage device of the AMS 10 and associated with the profile 28 of thatbattery.

In some embodiments the assets 11 may be a fleet 13 of batteries or asubset of the fleet 13 of batteries. A fleet 13 of batteries may be usedin a variety of situations, including healthcare facilities,manufacturing facilities, malls, department stores, warehouses, etc. TheAMS 10 is configured to provide real time analytics on the charge andhealth of the fleet 13 of batteries. In other embodiments, the AMS 10can be used to track a variety of other assets 11, including computerstations, kiosks, tools, or any other device suitable and known to oneof skill in the art.

In other embodiments the AMS 10 may include a variety of othercomponents, such as memory storage devices, receivers, transmitters,transceivers, specialized sensors, internet capable components, anyother device suitable and known to one of skill in the art. A memorystorage device may be configured to store a variety of asset conditionswhich are monitored by sensors. The memory storage device may beaccessed by the processor 14 to determine certain metrics for which theAMS 10 is monitoring.

The sensors 24 are operably coupled to the system via a wired orwireless connection. The connection may occur over a network 15 such asa local area network (LAN), wide area network (WAN), virtual LAN (VLAN),virtual private network (VPN), or any other wireless protocol such asBluetooth™ Low Energy (BLE), near field communication (NFC), or anyother method suitable and known to one of skill in the art. The sensorssend data via a transmitter or transceiver 23 when certain conditionsare met by the asset to trigger a response from the sensor. The AMS 10,including a processor 14, then takes the data from the sensor andinterprets the data and outputs analyzed data. The analyzed data may beoperable to be displayed on a user interface to a user.

Predictive Health of Battery Assets

The next aspect of the disclosure relates to the predictive health ofbattery assets based on current health levels, battery decay curves, anda variety of predictive inputs which are measured and used indetermining predicted health levels over future periods of time. Nowreferring to FIG. 7, predictive battery health may be calculated for abattery or a fleet 13 of batteries, which may be displayed on apredictive calendar 107.

As previously discussed, current battery health may be determined as aratio of the current FCC divided by the AFCC of a battery. Thisdetermination allows for a predictive model for battery health suchthat, in an embodiment, 6-month and 12-month predictions (or any otherincrement of time) can be made to enable planning and organization ofresources based on accurate, dynamic, data-driven models.

Battery health degradation is based on the known, published batterydecay curves, adjusted for both the general degradation of the batterypacks used, and degradation of the specific battery pack as opposed todegradation of the general battery packs, considered across the entirefleet. The decay curve may be periodically adjusted based on actual datacollected from the fleet, the specific battery, and updates from thebattery manufacturer.

In an embodiment, use of the battery pack is continually monitored andapplied to the degradation curve to predict when the battery pack willhit certain health levels and is periodically updated based on changesin use and other factors as determined by sensors 24 operably coupled tothe batteries.

In some embodiments, the AMS 10 may determine the battery healthdegradation based on the known, published battery decay curves, adjustedfor both the general degradation of the battery packs used, anddegradation of the specific battery pack as opposed to degradation ofthe general battery packs, considered across the entire fleet 13. Thecurve is periodically adjusted based on actual data collected from thefleet 13 via the sensors 24 on each battery and updates from the batterymanufacturer. The processor 14 receives the data generated by thesensors 24 on the battery and integrates that data into the degradationcurves and any other data that may be used to update the model forpredictive health of a battery. This provides an up-to-date or currentrepresentation of the health with its predictive health in such a waythat a user may accurately anticipate the need for replacement, repair,or for general asset control purposes. In one embodiment, the decaycurve is used to predict a dates at which the batteries will fall belowa specific health threshold using full charge capacity readings, usagepatterns such as starting charge, depth of discharge, and ending chargelevel, and cell imbalances using voltages and/or impedance of cellsand/or groups of cells.

In some embodiments, the use of the battery pack is continuallymonitored and dynamically applied to the decay curve to predict when thebattery pack will hit certain health levels. This application of thedegradation curve is periodically updated based on changes in use.Changes in use may vary, but include and are not limited to discharginga fully charged battery versus discharging a partially charged battery,completely discharging a battery versus only partially discharging abattery prior to recharging, or power demands on the battery.

In other embodiments, each battery has an associated predictivedegradation curve, which may be expressed in either a formula or table.The curve is based on age of the cells, number of cycles used, androlling average current draw while discharging. This curve is generatedby providing a baseline with the curve provided by historicalperformance of the fleet, adjusting the curve for ongoing fieldexperience, and adjusting each battery curve, individually, for the fitof that battery to the curve based on the above cited three (3) factors.

Based on the rate of usage, age, and current draw, the system assigns apredicted time or time range in which each battery will reach varioushealth levels. When the predicted health of a battery, based on themetrics described above, drops below a set percentage of battery health,the battery is added to a report tabulating those batteries which mayneed maintenance and/or replacement, and the expected date it would hitthe threshold. The system allows for preemptive determination of batterywear and fleet size, without the problems described in the current stateof the art, above. FIG. 7 demonstrates an exemplary embodiment of acalendar listing dates by which batteries are predicted to reach or passa certain threshold of battery health.

Any number of metrics may be used to provide a more accurate predictivehealth of an battery and may not be limited to only type of use, age ofbattery, cycle count, battery error codes and faults, batterytemperature, depth of discharge, ending charge, cell imbalances,voltages and/or impedances of battery cells or groups of cells, and anyother data point that would be used by one of skill in the art. The AMS10 described for monitoring and predicting health of the batteryprovides not only a display of these metrics and data points, but theAMS 10 provides accurate representations of potential fail timelines,predictions for replacement dates based on efficiencies, and predictedhealth thresholds for a future date. Prediction of battery life and lifecycle is difficult, especially in a system where a fleet of batteries isused and can be used in a wide variety of different ways including quickdischarges, partial discharges, varying operating environments includingtemperature, and many other variable settings and ways in which abattery may be implemented. The AMS 10 provides for a dynamically andaccurately updated prediction of battery health and battery lifethresholds. See FIG. 18 for a general overview.

The AMS 10 takes device specific information from a battery and appliesit to a known degradation patterns to yield predictive health data.Thus, the prediction is based on data specific to the battery which theAMS 10 is evaluating separately because each battery may be experiencingvarying use patterns and conditions. The system may also provide for theintegration of data across several devices that share similarcharacteristics or circumstances of use to create an average predictivehealth for a battery, a sub-fleet, or a fleet of batteries, which may bepractical for certain units or departments which use the same devicesunder similar circumstances and conditions. This allows situationspecific predictions of battery health levels over time yielding uniqueand accurate timelines for each battery in the fleet of batteries.

In some embodiments, any data that is stored on a battery or storedregarding a battery from sensors 24 on or near a battery may be used asan input into the decay curve to predict a future health of the battery.Thus, data is not merely stored and presented to a user, but the AMS 10is predicting outcomes and may provide recommendations based on thepredicted outcomes. Such recommendations may include replacement dates,reallocation of batteries, and replacement of types of batteries with asecond type of batteries that are better suited for a specific use.

In one embodiment, the AMS 10 includes a plurality of assets, aplurality of sensors 24, each sensor of the plurality of sensors coupledto separate assets 11 of the fleet 11 of assets, the plurality ofsensors 24 operable to sense an asset condition and generate a signal ateach instance when the asset condition is met across the plurality ofassets. Alternatively, multiple sensors 24 of the plurality of sensorsmay be coupled to each asset 11 of the fleet 13 to assets. A processor14 is operable to receive the signal and a secondary signal, wherein thesecondary signal comprises a decay curve representing a predicted healthlife of each asset of the plurality of assets 11 and wherein theprocessor 14 is operable to integrate the asset condition 29 into thedecay curve and generate an updated predicted health of each of theassets of the plurality of assets. A display is operable to display theupdated predicted health of each of the assets 11 of the fleet 13 ofassets In some embodiments, the processor 14 is resident on the AMS 10,and in another embodiment, the asset processor 25 is resident on each ofthe assets 11, to produce the predicted health of each asset. In thepreferred embodiment, the AMS 10 is operable to receive all of theinformation, including the asset conditions, the data from the sensors,and the decay curves and use all of this data to produce a predictedhealth for each asset which can be associated with the asset profile 28of the AMS 10. Each asset 11 may be operable to relay information to theAMS 10 via a network 15, including the data generated by the sensors 24and other data relating to asset conditions and specifications. Thesensors 24 may include cycle counters, voltmeters, ammeters,thermometers, and any other sensor known to one of skill in the art.

In another embodiment, a method of managing, monitoring, and allocatingassets 11 in a facility includes detecting an asset condition viasensors 24 coupled to each asset 11 of a plurality of assets andoperable to detect when the asset condition is met. The method includesgenerating a signal based the asset condition being satisfied,transmitting the signal to a processor 14, aggregating a plurality ofsignals via the processor 14, assigning a plurality of visualrepresentation data to each of the plurality of signals, arranging theplurality of visual representation data based on the asset condition ofthe plurality of assets, and displaying the visual representation dataon a display.

Predictive health can be presented in a heat map format, report, orother forms, using various forward-looking representations for theprediction of the battery health and life. In one embodiment, batteryhealth percentage is represented on a heat map. “Red” batteries arebelow that cut off percentage of minimum battery health, with “green”down to “orange” showing how close each battery is to recommendedreplacement time, with orange-shaded cells closer to recommendedreplacement than green. Other methods for displaying the data may beimplemented as previously discussed.

In some embodiments, data collected and stored relating to each batterymay be relayed to the server 20 when the battery 11 b is docked in acharging station or is in use on a mobile workstation. The data may betransferred via the existing communication networks provided by thecharging stations and mobile workstations. See FIG. 8.

As can be seen in FIG. 18, predictive battery health may include “DetectFull Charge Capacity (FCC) of a Battery” 181, “Store FCC Data over aPeriod of Time” 182, “Determine Average/Manufacturer Full ChargeCapacity (A/MFCC)” 183, “Determine Battery Health Level based on CurrentFCC relative to A/MFCC” 184, “Generate Heat Map of Battery Health Levelsfor Fleet of Batteries” 185, “Sense Events and Conditions Relevant toBattery Health” 186, “Adjust Known Decay Curve based on Sensed Eventsand Conditions” 187, and “Determine Predicted Dates for Battery PassingHealth Level Thresholds” 188.

In a possible embodiment, the heat map 16 can also be used as a visualpredictive model to determine how many units within a fleet 13 may needreplacing at a given time interval in the future. In one example, radiobuttons and/or sliders adjust the time perspective of the heat map, fromthe present time into the future. Selection of a 6-month prediction, forexample, would show the prediction of the health heat map 6 months fromthe current date based on the previously described determinations.

In some embodiments, a method for managing a plurality of batteries andpredicting battery health of the plurality of batteries in a facility isalso provided. The method may comprise determining a full chargecapacity of a battery over an initial period of use, wherein the fullcharge capacity is determined each time the battery is fully charged,generating data comprising the full charge capacity of the battery foreach time the battery is fully charged over the initial period of use,transmitting the data to a processor 14, determining by a processor 14an average full charge capacity of the battery based on an average ofthe data, determining a current health level of the battery by dividinga current full charge capacity by the average full charge capacity,receiving a battery health decay curve, integrating the current healthlevel of the battery into battery health decay curve, and generating apredictive decay curve.

Return on Investment of a Fleet

The next aspect of this disclosure relates to determining the return oninvestment (“ROI”) of individual assets 11 or a fleet 13 of assets basedon usage and usage patterns. The ROI portion of the present disclosureprovides users with information relating to the use of an asset 11during defined periods of time for consumption by the user as well asrecommendations for allocation and use of assets 11 in a facility. ROIas presently disclosed provides the usage of an asset 11 during hours inwhich a fleet 13 of assets are being heavily used and not just anaverage use of the asset 11 over a day or month, thus providing a moreaccurate picture of the needs for assets and allocation of assets tomeet those needs within a facility. Included in the disclosure is anovel system and method for determining when an asset 11 is in use andnot in use. As can be seen in FIG. 19, one embodiment of determining ROIcan include the steps of: “Detect Use of Asset” 191, “Determine UseState of Asset Based on Detected Use” 192, “Determine Periods of HighUse for a Fleet of Assets” 193, “Assign Assets a HighReturn-on-Investment Status which have an In-Use State During aThreshold Number of High Use Periods” 194, and “Generate a Heat Map ofROI for a Fleet of Assets” 195. The AMS 10 is also operable to providerecommendations as shown in FIG. 19 as: “Detect Use of Asset” 191,“Determine Use State of Asset Based on Detected Use” 192, “DeterminePeriods of High Use for a Fleet of Assets” 193, “Assign Assets a HighReturn-on-Investment Status which have an In-Use State During aThreshold Number of High Use Periods” 194, and “Provide Recommendationsfor Reallocation of Assets and Fleet Management” 196.

ROI can be measured over any period (such as daily, 7-day, 14-day or anyother suitable measurement period). Within those parameters, any asset11 that is in use in any of the “High Use Periods” is flagged as “HighROI”. Thus, ROI is the percentage of how many “High Use Periods” inwhich a given asset 11 (or group of assets) is used as a ratio of thetotal number of “High Use Periods” in the measured timeframe. ROI allowsa user to determine, using data driven models which are powered bydynamic updating of asset use, if the assets 11 are being usedeffectively, how to reallocate resource for more effective use, and whenadjustments need to be made within a fleet including supplementing thefleet with additional assets.

For each mobile workstation and battery, an ROI determination iscontinually performed. This is based on the use during a given period,compared to a target usage as represented by a fully used asset duringpeak periods of a day.

Certain assets 11 can be designated as HIGH ROI based on their intendeduse in a facility (e.g. Emergency Room, OR, etc.). Each day can bemeasured hour-by-hour to determine if a given asset is in use duringeach hour or not. Peak usage hours can be established for eachdepartment, based on the maximum assets in use for each hour of the day.When a workstation is in use during peak usage hours, then it is flaggedas HIGH ROI. Batteries are flagged as HIGH ROI if they are used duringpeak usage hours, or used in a workstation anytime within the next 24hours.

In another embodiment, a user may manually profile a department for peakusage hours based on specific needs associated with the department. Forexample, the emergency room may profile a specific day of the week suchas Saturday as a peak usage of assets 11. In other embodiments, theprocessor 14 is able to analyze the usage data associated with eachasset profile 28 to determine when assets 11 are being utilized anddetermine the peak usage hours based on the actual use as read bysensors 24 operably coupled to each of the assets 11.

Over the course of time, each asset is given a rolling average ROI basedon how many days it is flagged as HIGH ROI as opposed to days notflagged as HIGH ROI. In one embodiment, assets are color-coded into achart or graph showing the ROI over user-selectable time frames (day,week, 14 days, etc.—up to 12 months).

In an embodiment, device use states can be defined as either “directlyin use”, “at the ready”, “under the control of a clinician” (notavailable for someone else to use), “in-transit between use points” (formobile devices), or “not in use”. Such classifications enable indirectmonitoring of the fleet.

Use states may be determined via various sensors 24 coupled to an asset11. The sensors 24 may include voltmeters, ammeters, power consumptionmeters, accelerometers, 3-axis gyroscopes, 6-axis gyroscopes,magnetometers, key stroke monitors, vibration sensors, proximitysensors, NFC readers, RFID readers, and any other sensors known to oneof skill in the art. The use of these some of the these sensors will bedescribed in further detail below for determining how a use state of anasset 11 may be determined based on the data generated by each sensor inresponse to an condition being met.

Specifically, certain embodiments may include a variety ofconfigurations of sensors 24 for determining the use state of an asset11. In one embodiment as shown in FIG. 20, an asset 11 a may be equippedwith a voltmeter 24 a, an ammeter 24 b, a power consumption meter 24 c,an accelerometer 24 d, a 6-axis gyroscope 24 e, a magnetometer 24 f, akey stroke monitor 24 g, a vibration sensor 24 h, a proximity sensor 24i, an NFC reader 24 j, and an RFID reader 24 k, wherein each sensor 24is operable to determine when conditions occur involving certain uses ofthe asset and to communicate those events to the AMS 10. A second asset11 b may be equipped with a voltmeter 24 a, an ammeter 24 b, a powerconsumption meter 24 c, a 6-axis gyroscope 24 e, and a proximity sensor24 i, wherein each sensor 24 is operable to determine when conditionsoccur involving certain uses of the asset and to communicate thoseevents to the AMS 10. A third asset 11 a may be equipped with a powerconsumption meter 24 c and an accelerometer 24 d, wherein each sensor 24is operable to determine when conditions occur involving certain uses ofthe asset and to communicate those events to the AMS 10. It is withinthe scope of this disclosure that any asset may be a combination ofthese sensors 24 may be present on an asset, however, several othervariations of sensors 24 on an asset 11 will be specifically mentionedin the following examples.

In another embodiment as shown in FIG. 21, an asset 11 a may be equippedwith a power consumption meter 24 c, a 6-axis gyroscope 24 e, amagnetometer 24 f, a vibration sensor 24 h, and a proximity sensor 24 i,wherein each sensor 24 is operable to determine when conditions occurinvolving certain uses of the asset and to communicate those events tothe AMS 10. A second asset 11 b may be equipped with a power consumptionmeter 24 c, a 6-axis gyroscope 24 e, a key stroke monitor 24 g, avibration sensor 24 h, and a proximity sensor 24 i, wherein each sensor24 is operable to determine when conditions occur involving certain usesof the asset and to communicate those events to the AMS 10. A thirdasset 11 a may be equipped with a 6-axis gyroscope 24 e and a proximitysensor 24 i, wherein each sensor 24 is operable to determine whenconditions occur involving certain uses of the asset and to communicatethose events to the AMS 10.

In another embodiment as shown in FIG. 22, an asset 11 a may be equippedwith a power consumption meter 24 c, a 6-axis gyroscope 24 e, amagnetometer 24 f, a vibration sensor 24 h, and an NFC reader 24 j,wherein each sensor 24 is operable to determine when conditions occurinvolving certain uses of the asset and to communicate those events tothe AMS 10. A second asset 11 b may be equipped with a power consumptionmeter 24 c, an accelerometer 24 d, a magnetometer 24 f, a key strokemonitor 24 g, and a vibration sensor 24 h, wherein each sensor 24 isoperable to determine when conditions occur involving certain uses ofthe asset and to communicate those events to the AMS 10. A third asset11 a may be equipped with a power consumption meter 24 c and a proximitysensor 24 i, wherein each sensor 24 is operable to determine whenconditions occur involving certain uses of the asset and to communicatethose events to the AMS 10.

In one embodiment, direct use states can be measured indirectly, so asnot to incidentally capture Patient Information Act or Health InsurancePortability and Accountability Act controlled data. Direct use can bemeasured via power use measurements. These measurements are dynamic,seeking whether a particular device is in a non-use norm, or whether thedevice exceeds the threshold power usage used to determine active use ofthe device. In this manner, an addition of accessories does not requirea manual recalibration of baseline power use. A power use sensor 24 a isoperably coupled to the asset 11 so as to determine the power use of theasset 11 without actually capturing the data or information being usedby the asset 11. For example, power usage may be monitored on a mobileworkstation by monitoring how much power is being consumed by theworkstation when a clinician is directly interacting with theworkstation versus when the workstation is standing idle. Because theAMS 10 is dynamically updating the power usage, when additions ofaccessories to an asset 11 occurs, the AMS 10 is able to determine theuse state of an asset 11 even when the power consumption and powerrequirements have been altered by the addition of the accessories.

In some embodiments, the back current draw is used to determine the usestate of an asset 11. The AMS 10 may be operable to detect varying bandsof current draw from an asset 11 or a plurality of assets. In oneexample, when a station is in an off-state, minimal power will be drawnfrom a power source. Thus, if the power drawn from the power source iswithin a predetermined range or band of power characteristic of a devicein on off-state, the asset 11 may be assigned a “not in use” designationfor that time interval. Different tasks on different devices havevarying power demands. Therefore, an asset 11 can be characterized basedon the power demands and the type of asset requiring the demands. Asensor 24 is operably coupled to the asset 11 and specifically the powerinterface between the electronic components and the power source suchthat the sensor 24 is capable of detecting the back current draw. Thesensor 24 is operable to send a signal to a processor 14. In someembodiments the signal contains data packets regarding the power use orback current draw of the asset 11. The processor 14 is then able tointerpret the signal and able to determine by a proprietary algorithmthe use state of the asset 11. A memory storage device is operable tostore the signal or data packets from the sensor as well as theinterpreted data and signal from the processor 14.

Furthermore, the AMS 10 may determine if multiple assets 11 are beingutilized on a station. The AMS 10 is able to determine the power demandson specific ports of a workstation. In one example, a printer may beconnected to the AMS 10 via a USB port on the workstation. The AMS 10 isable to detect the power demands from the USB port and determine the useof an accessory, auxiliary, or peripheral device on the workstation. Theuse of accessory, auxiliary, or peripheral devices may also beclassified based on bands or ranges of power demands. Power demands maybe measured by current or voltage demands, or any other method known toone of skill in the art.

As the power demands of a workstation and the accessory, auxiliary, orperipheral devices associated therewith are recorded and stored, the AMS10 is able to assign use states to the workstations and devices. The usestates and patterns may be associated with a profile 28 of each asset 11stored on a memory storage device. The use and patterns of use mayfurther be implemented to manage and allocate resources within a fleet.Based on the information, the AMS 10 may make recommendations to moreefficiently allocate resources. In one example, if an EmergencyDepartment has ten (10) workstations, all of which have been assignedthe status of HIGH ROI based on direct use during peak usage periods,whereas the Intensive Care Unit has twenty (20) workstations, but onlyten (10) of the workstations are assigned the status of HIGH ROI duringpeak usage periods, the AMS 10 may recommend that five (5) of theIntensive Care Unit's mobile workstations be reallocated to theEmergency Department. The AMS 10 may also recommend the procurement ofmore devices if all devices in a department or fleet have been assignedthe status of HIGH ROI in order to prevent downtime due to devicefailure or maintenance.

Returning to use states, direct use states or use states can be measuredusing asset location and proximity data. In one example, “At the Ready”is measured via location of the device and proximity of a givenclinician to the asset. Location of an asset 11 and the proximity of aclinician to the asset 11 may be determined with location devices 12deployed on the asset and the clinician. In one example, a mobileworkstation may include an RFID or NFC reader 24 f which is operable todetect an NFC tag or RFID chip which has been placed in a clinician'sidentification badge 52 or by NFC and RFID in a mobile phone 53, asshown in FIG. 9. When the clinician is within the operable proximity ofthe mobile workstation's RFID or NFC reader, the RFID or NFC readerauthenticates the clinician via a network 15, update a server 20 of theproximity of the clinician to the mobile workstation. When this occurs,the AMS 10 is able to update the use state of the mobile workstation to“At the Ready.” In another example, the RTLS 30 portion of the AMS 10 asdescribed previously may be deployed to locate a mobile workstationwithin a facility, wherein the location data 26 is stored, dynamicallyupdated, and associated with an asset profile 28 in the AMS 10. Asimilar method of tracking clinicians may also be implemented. When thisoccurs, the AMS 10 may monitor the location of a clinician relative tothe mobile workstation. When the AMS 10 determines that the clinician iswithin a threshold proximity of the mobile workstation, the AMS 10updates the asset profile 28 for the mobile workstation to “At theReady” based on the relative location of the clinician to the mobileworkstation.

As another example, “In-Transit” is directly measured viaaccelerometers, distance counters and other such transducers anddevices. In other embodiments, “In-Transit” may be determined based onlocation data 26 associated with the mobile workstation from one momentto the next. If the AMS 10 determines that the location data 34associated with the asset profile 28 demonstrates that the mobileworkstation has moved to a second location during a period of time, theAMS 10 may update the use state of an asset 11.

“In-Use” is determined on a periodic basis, and each device isidentified as either being in use or not in use for that measuredperiod. A variety of different types of use states may be readilyapparent to one of skill in the art. The system is operable to determinewhich use states will be profiled as “In-Use” and “Not-in-Use.”

In some embodiments, the day is divided into increments of time(including but not limited to hours or minutes or seconds). Any asset 11in use for one or more measures during that increment is assigned adirect use state of “In Use” for that increment. Any asset 11 thatrecords any use state other than “Not-in-Use” during an increment isassigned an “In-Use” state. In other embodiments, an asset 11 isassigned the state in which the asset 11 was for the majority of theinterval. In one example, when the interval is selected at one hourincrements, when an asset 11 is “Directly-in-Use” for 28 minutes,“In-Transit” for 5 minutes, and “Not-in-Use” for 27 minutes, the asset11 will be assigned a state of “directly in use” for the hour interval.In other embodiments, the asset 11 will be assigned a state of “In-Use”for the hour interval. The two examples vary in the assignment of stateunder the same set of facts as the first example is more specific to thestate the asset 11 was assigned based specifically on the state of thedevice during the hour interval, whereas the second example demonstratesthe assignment of a state based on the combined totals of specificcategories, which together form a broader category of use. Thus, in thesecond example, the assignment of “Directly-in-Use” and “In-Transit” arecombined into a single category defined as “In-Use.”

In one embodiment, data for each asset 11 is placed in a usage table 110and is assigned a state of “In-Use” or “Not-In-Use” for each asset 11and each increment or period of time, as seen in FIG. 10. For eachperiod, the number of devices (either in total or grouped by work areaand/or type of equipment) is counted and compared to the availableassets within the same grouping. This data may be displayed in tablesand/or charts. Workstation Fleet ROI may be displayed in a heat mapformat, as in FIG. 11, or a column bar format 112, as in FIG. 12.

In some embodiments, a subset of increments are flagged as high-useperiods. Maximum usage may be identified in order to assess whether afacility has an appropriate number of assets 11, in total or by group,and for ROI determinations.

In other embodiments, ROI data may be integrated into battery healthdata (or asset condition data) 29 and can be used to develop predictivemodels of battery lifespan. In one example, the use states of an asset11, for example a battery, may be used in the algorithm for determiningthe predicted functional lifespan of the battery. If a battery isconstantly in a “In-Use” state, the predicted lifespan of the batterymay be shortened. Thus the use state data 27 that is associated with theprofile 28 of an asset 11 may be used as an additional factor in thedetermination of asset health or predicted asset health.

Device ROI can be determined over any period (such as daily, 7-day,14-day or any other suitable measurement period). Within thoseparameters, any device that is in use in any of the “High Use Periods”is flagged as “High ROI”. Thus, ROI is the percentage of how many “HighUse Periods” in which a given device (or group of devices) is used as aratio of the total number of “High Use Periods” in the measuredtimeframe. In one example, a subgroup of a fleet 13 of mobileworkstations may be deployed in a surgical unit of a hospital. Thesubgroup of the fleet of mobile workstations may include 10 mobileworkstations. Over the course of a week period the system monitors theuse state data 27 associated with each of the mobile workstations anddetermines when the most mobile workstations are determined to be“In-Use.” For those periods when the most mobile workstations are“In-Use” the system designates those periods as “High Use Periods.” TheAMS 10 then analyzes the use state data 27 associated with each of themobile workstations during the high use periods. A first mobileworkstation may have been assigned an “In-Use” state during 7 of thepast 10 “High Use Periods.” The mobile workstations may be assigned a70% ROI based on the number of times the mobile workstations was“In-Use” during the “High Use Periods.”

In one embodiment the ROI data may be based on flagged high use assets.This allows the AMS 10 or a user to allocate assets 11 throughout afacility more efficiently based on the ROI data. In one example, if anasset 11 is being used only intermittently in one department whereasanother department has a lack of assets, the asset 11 may be reassignedto the department with insufficient assets. Likewise, the AMS 10 mayrecord use patterns and determine certain types of assets that may bebetter suited to the type of use of another department. In one example,a battery with a high charge capacity may not be efficiently used in adepartment which uses the battery for short periods, where a battery maybe charged in the intervals between the short periods of use. Thebattery with a higher charge capacity may be allocated to anotherdepartment or for types of use that requires use over long periods oftime without recharging. Another example might be a battery with thevoltage and current capacity to power several devices is allocated to astation that powers several devices whereas a lighter battery may beallocated to a mobile station.

In some embodiments, the ROI data for an asset 11 may be used tomodulate or allocate assets within the fleet based on indirect data ordirect use state. Certain assets 11, either individually or in groups,are not available to be reallocated, and are defaulted to “High ROI” foreach measured period. In a given embodiment, these assets 11 will have a100% ROI for the measured period so as to not indicate available assets11 when they are, in fact, not available for use elsewhere in afacility. ROI may be determined and put into a table where it can bedisplayed via reports or various displays, including those previouslydescribed.

In a further embodiment, the AMS 10 is able to make recommendationsbased on the use of accessory, auxiliary, or peripheral devices. Becausethe AMS 10 may track the use of accessory, auxiliary, or peripheraldevices, the AMS 10 may determine when an accessory, auxiliary, orperipheral device is being underutilized and may be more efficientlyutilized on a different workstation. For example, a workstation may beconnected to a printer. If the printer is being underutilized accordingto a predetermined threshold or comparative data from otherworkstations, the system may recommend reallocating the printer to adifferent workstation or a different department.

In other embodiments, the AMS 10 may use current draw data to flagassets in need of maintenance. In some instances, an asset 11 may beimproperly functioning, and based on the type of current draws from astation, the system may be able to detect that the asset is improperlyfunctioning. For example, a mobile workstation may have abnormal powerconsumption. The AMS 10 may detect this abnormal power consumption andflag the mobile workstation. In other embodiments, a clinician may knowthat a mobile workstation is improperly functioning but has not made areport regarding the defect in the mobile workstation. The AMS 10 isable to detect abnormal patterns of use based on a proprietary algorithmwhich triggers the AMS 10 to set a maintenance flag on the mobileworkstations profile 28.

In some embodiments, patterns are recognized over long periods of time.Thus periods of high use for an asset in a department may not only belimited to certain times of the day or days of the week. In one example,the AMS 10 may be able to track use data over several years andrecognize patterns of high demand in certain departments, such as higheruse in Labor and Delivery during summer months, higher use in theEmergency Department around holidays and football season, higher use inFamily Medicine Department during flu season, etc. Thus, the AMS 10 isable to reallocate resources during certain busy seasons based onprevious trends determined by the data recorded, stored, and analyzed bythe AMS 10.

In some embodiment, the patterns of use of individual assets and groupsof assets 11 establish a baseline or normal use pattern. When an asset11 falls outside that use pattern, the AMS 10 can flag the device. Anasset 11 may be flagged due to a failure or another situation thatresults in the asset 11 being unavailable for use. A normal use patternmay be an aggregation of data recorded on the asset 11. In otherembodiments, it may be a manufacturer recommended use. Analytics withinthe AMS 10 may be run to optimize the use of the devices and assets 11to allocate them in the most efficient manner. When an asset 11 fallsoutside a threshold of “normal use”, an alert may be created.

In another embodiment, once an asset 11 is flagged as a problem, the AMS10 may perform one or more functions based on the flag. The AMS 10 maysend notifications by various means such as email, on-screen notices,and other alerts to individuals responsible for maintaining andrepairing the devices. The AMS 10 may trigger or activate a screen or anoptical/audible indicator of the flagged device as an alert, includingmessages such as “Check Cart”, “Check Battery”, or other checkindicators. Such indicator can be an illumination or an icon.

In another embodiment, the AMS 10 may read fleet and network-levelpatterns to enable metric tracking. For example, a “Normal Pattern” canbe established over time, using the data received and stored regardingthe usage level of the asset 11 or group of assets. When the usage isperiodic, a shorter time period is required to establish a reliablebaseline of use.

In some embodiments, an ROI determination may be continually performedfor each workstation and battery. The ROI determination may be based onthe use during a given period, compared to a target usage as representedby a fully used asset 11 during peak periods of a day. Certain assets 11can be designated as or assigned a “HIGH ROI” status based on theirintended use in a facility (e.g. Emergency Room, OR, etc.). Each day canbe measured hour-by-hour to determine if a given asset is in use duringthat hour or not. Peak usage hours can be established for eachdepartment based on the maximum assets 11 in use for each hour of theday.

In some embodiments, if a workstation is in use during peak usage hours,the workstation is flagged as HIGH ROI. Batteries are flagged as HIGHROI if the batteries are used during peak usage hours, or used in aworkstation anytime within the next 24 hours. Over multiple increments,each asset is given a rolling average ROI based on how many days theasset is flagged as HIGH ROI as opposed to days not flagged as HIGH ROI.In one embodiment, assets are color-coded into a chart or graph showingthe ROI over user-selectable time frames (for example: a day, week, 14days, etc.).

In one embodiment, the AMS 10 is provided for managing, monitoring, andallocating assets 11 in a facility, including a plurality of assets 11,a plurality of sensors 24, each sensor 24 of the plurality of sensors 24coupled to an asset 11 of the plurality of assets, the plurality ofsensors 24 operable to sense an asset condition and generate a signalwhen the asset condition is met, a processor 14 operable to receive thesignal and generate a heat map 16, wherein the heat map 16 is arrangedsuch that a plurality of signals are assigned a color to represent theasset condition and each color is grouped with a group of like colors,and a display operable to receive the heat map 16 and display the heatmap 16. In some embodiments the AMS 10 may be configured to determineROI.

In one embodiment, the ROI data can be used to determine the probabilityof a workstation having already failed (become unusable) or approachingfailure, allowing for preemptive ordering of stations or maintenance.

In another embodiment, a method of managing a plurality of assets 11 anddetermining a ROI of the plurality of assets 11 comprises sensing use ofplurality of assets 11 during a period of time, determining high useperiods for the plurality of assets 11, and flagging high use assets ofthe plurality of assets 11 having a threshold use during the high useperiods.

In one embodiment, current failure is determined by comparing therolling use average of a workstation, during the busiest hour or hoursof the day against the current use of the workstation. A gate test canthen be used to evaluate an average workstation use (across a departmentor fleet) during the peak use hour or hours, and compare the averageworkstation use to each workstation's current use to see if current usefalls below a predetermined percentage of the workstation's average use.A workstation that fails this test is flagged as “likely broken” andnotifications are sent and a status is set to indicate the likelycondition.

In some embodiments, the workstation use-percentage is determined forpeak use hour or hours and a running average is retained. If the currentuse falls below a dynamic band around the running average, a workstationis flagged. The AMS 10 provides a prediction of when the workstationwill no longer be usable based on the data and trend recorded by the AMS10. In some embodiments, the AMS 10 uses a proprietary algorithm topredict the failure date. That predicted date may be mapped to a colorheat map 16.

In some embodiments, the predicted current failure and predicted futurefailure (a time frame configurable by a user or manufacturer) isindicated on the user interface or workstation screen (touch screen, PCscreen, etc.), via a “check workstation” icon as depicted in FIG. 4.Additionally, certain measurements (including, but not limited to,voltage, current, temperature, accelerations, impacts, loads, and otherfactors) will trigger the check workstation indication and send anotification to the customer and service department of the manufacturer.Use-hours, distance, or a combination of use-hours and distance cantrigger the check workstation indicator. Indicators can be used onscreen, in email and other messages, and in reports to indicateworkstations that are in need of service.

In another embodiment, when an asset has been flagged or is otherwise inneed of technical assistance, repairs, or attention from IT, the systemallows for a computer on a mobile workstation to be remotely rebooted.Thus an IT tech does not need to be deployed directly to the mobileworkstation to perform a reboot of the system. The system is alsooperable to perform remote troubleshooting of assets 11.

Graphical User Interface

A next aspect of the disclosure includes a graphical user interface 32for providing a user with a dynamic snapshot of assets 11 and a fleet 13of assets including health, charge, location, ROI of assets 11, andrecommendations based on real-time data for allocating resources andmaintaining an effective and efficient fleet 13. The disclosure furtherprovides for a graphical user interface 32 capable of providing dynamicanalysis of a fleet of assets 11 in the context of computerized assetmanagement. Various embodiments of the graphical user interface 32 havepreviously been described.

In one embodiment the graphical user interface 32 provides a home screen113 in which a variety of information may be displayed and is alaunching point for all of the activities within the AMS 10, as seen inFIG. 13. The graphical user interface 32 may provide a main menu,dashboard views and graphs 34, case status 44 and contact informationincluding communication channels, recent notifications, assets ininventory summary 48, and current case information 46. The informationdisplayed in each feature and module of the graphical user interface 32may be dynamically updated to provide a user with real time analysis ofthe status, health, and usage of a fleet of assets. The home screen mayinclude a display of battery information, including battery health andbattery charge level. As discussed above, battery health and batterycharge level may be displayed in a heat map 16. The AMS 10 candynamically update the heat map 16 based on the data associated witheach specific battery's profile 28. For example, as the AMS 10 receivescharge data on a battery that is actively being discharged, the chargelevel of the battery might drop below a second charge level of a secondbattery. The AMS 10 dynamically updates the position of therepresentation 18 of the battery relative to the position of the secondrepresentation of the second battery. Furthermore, the AMS 10 isoperable to dynamically update the representation 18 as the batterycharge level or health drops below a specific threshold. For example,when a battery charge level drops below 35%, the graphical userinterface 32 is updated such that the representation of the battery isprovided as a red rectangle. See FIG. 2 and FIG. 6.

The home screen of the graphical user interface 32 may also be operableto provide a graphical representation of the assets 11 in inventory fora fleet of assets. The representation may specifically divide the assetsup by the types of assets, such as workstations and batteries, as wellas by make and model.

Referring to FIG. 13 and FIG. 14, dashboard views 34 may be provided onthe graphical user interface 32 which includes dynamically updatedinformation and snapshots of the fleet 13 and assets 11 of the fleetincluding run time of the assets, mobile workstation usage, batteriesused in that day, battery inefficiencies, and more detailed informationdisplayed on each of these views when by hovering the cursor over aportion of the dashboard views, an example of the hovering can be seenin FIG. 6. This provides a fleet 13 overview including the status anduse of the assets 11 in the fleet, providing a dynamic picture of theuse and health of the fleet 13 and assets 11.

Each representation of a battery may be embedded with additionalinformation that can be accessed via a link or an action. For example, auser may hover a cursor over a specific battery and the graphical userinterface can display the battery identification number, the chargelevel, or any other data that might be relevant, as seen in FIG. 6. If auser selects the representation, the AMS 10 is operable to display moredetailed information or all of the information associated with thebattery and battery profile, as seen in FIG. 4.

The graphical user interface 32 may also be operable to displayinformation regarding the ROI of a fleet of assets, as seen in FIG. 11.The AMS 10 dynamically provides this information to the display toprovide a real-time snapshot of the use and allocation of assets 11within a fleet 13 of assets. As previously discussed, the AMS 10 isoperable to determine a ROI for specific assets 11 within a fleet 13 ofassets. The graphical user interface is able to provide that informationin a heat map format or a column bar format. As the ROI of certainassets 11 change overtime, the position of the graphical representations18 used to denote a specific asset 11 may be moved relative to anotherasset and the symbol 18 may be altered to represent a change, such asthe symbol changing from a yellow color to an orange color when an assetdrops below a threshold ROI. As discussed with regards to the batteries,the graphical user interface is operable to provide supplementalinformation when certain actions are taken. A user is able to determinewhich assets 11 may be underutilized and reallocate the asset 11 foranother purpose or to another department based on the ROI data. In otherembodiments, the AMS 10 is able to determine which assets 11 may beunderutilized and where the assets 11 may be more efficiently andeffectively used, such as in a department that has 100% ROI assigned toeach asset 11 assigned to that department.

The graphical user interface may also be operable to provide graphicalrepresentations of workstation utilization, as seen in FIG. 10.Workstation utilization demonstrates how many assets 11 in a fleet ofassets 13 are being utilized during specific time intervals. Forexample, the workstation utilization interface can display the number ofworkstations that are being used between 1:00 pm and 2:00 pm, includingthe numbers of workstations that have been used in the past day, thepast week, and the past month. The user can refine the display byselecting specific departments for which this data may be displayed.This allows a user to ascertain what periods of time the mostutilization and need of workstations might be during certain times ofthe day, including which departments. A user then may allocated variousassets 11 across the departments at different time periods based on thisinformation. In another embodiment, the AMS 10 may make recommendationson the allocation of assets 11 across different departments based on theworkstation utilization, as shown in the workstation utilization report115, one embodiment being depicted in FIG. 15.

The graphical user interface 32 may also be operable to provide agraphical representation of the open work orders relating to the fleetof assets. Each work order may be managed within the AMS 10 and can beaccessed from the home page. The AMS 10 is operable to integrate intoexisting IT systems and workflow, allowing existing ticketing systems tobe maintained. Information regarding case orders may be presenteddirectly on this page and more specific information may be accessedwithin the work order component, which can be directly accessed byselecting the desired work order on the graphical user interface.

The graphical user interface 32 may also be operable to displaygraphical representations of recent notification regarding the AMS 10and assets 11. It also provides an interface for communicating directlywith representatives for servicing the AMS 10.

The graphical user interface 32 may also be operable to displaygraphical representations of run time for workstations, use ofworkstations and batteries and inefficiencies of workstations andbatteries. Each of these representations may be selected to providespecific details regarding the assets for which the representations arepresented, including recommendations for reallocation, replacement, orsupplementing assets 11 within the fleet 13 of assets. Furtherdiscussion of how these determinations have previously been discussed inpreceding sections, including battery health and ROI.

The graphical user interface may also be operable to display specificinterfaces for viewing only battery information, only workstationinformation, and only charging station information, examples of whichcan be seen in FIG. 4 and FIG. 5. Within each of these interfaces,information may be sorted based on various factors including chargelevel, health, usage, cycle count, reporting wing and department,location, and other factors known to one of skill in the art.Furthermore, the AMS 10 provides an option to locate 36 the nearestbattery, workstation, or charger based on the location data by selectingan option on the graphical user interface. The AMS 10 is able to locate,based on the relative locations of the unit in use and the desired unit,where the closest desired asset 11 is located. The AMS 10 may provide aspecific location, and may also send a signal to the desired asset whichtriggers an audible or visible notification to be transmitted by thedesired asset 11.

An updates display may be provided on the graphical user interface whichincludes real-time updates and dynamically monitored status andrecommendations to a user. In one embodiment, this includes criticalupdates such as recommendations to replace assets or parts, servicingassets, added or removed assets 11 to the fleet, software updates on theassets 11, and any other notifications known to one of skill in the art.

The AMS 10 is operable to provide a dashboard status report, which isoperable to display to a user via the graphical user interface a dailysnapshot of the metrics of the system. The dashboard status reportincludes runtime of the batteries in the fleet 13, units used includingthe mobile workstations logging activity, batteries used including thecount of batteries logging a complete cycle, inefficiencies includinglogging events in which a battery was not fully utilized, peak includingthe time of day that shows the most activity from the mobileworkstations, dip including the time of day that shows the leastactivity from the mobile workstations, the average uptime of mobileworkstations from the previous day, daily average of uptime average ofmobile workstations, top five (5) hours of the day including the totalnumber of mobile workstations active during an hour and the total numberof workstations in the fleet, assets in inventory including a breakdownof types of assets, and charge levels and health of batteries in thefleet 13.

The AMS 10 is operable to provide via the graphical user interface auser with a six (6) month battery prediction based on the batteriesspecific operating condition and capacity of health, an example of whichis shown in FIG. 7. The battery planning calendar assists users withplanning for potential battery replacements in the fleet. This includesa projection of batteries that will fall below a health level thresholdduring a given month. Included in the projection via the graphical userinterface 32 for consumption by the user are the serial numbers of thosebatteries that will fall below a specific health level threshold. Theserial number also provides a link to the battery so the batteryinformation may be accessed via the graphical user interface whichallows a user to access related information such as warranty informationand dates associated with the specific battery as well as location data,including the ability to send a command to the battery via a wirelessnetwork 15 to emit a visual or auditory notification for locating thebattery.

Included in the graphical user interface is a workstation inefficiencyreport 116 which may be generated by the AMS 10 for consumption by theuser, an example of which is shown in FIG. 16. The workstationinefficiency report is operable to display inefficient events occurringin workstations throughout a fleet 13 of assets. The AMS 10 analyzes andcompiles the data provided by the mobile workstations to the server 20to provide a dynamically updated snapshot of the workstations with thehighest inefficiencies, including the number of inefficient sessions,the department, floor, and wing in which the inefficient sessionoccurred, and the average session start and end charge level. Theinefficiencies may also be partitioned based on departments.

Other elements of the graphical user interface include warrantyinformation, alerts, notifications, cases, dashboard results, users, andsettings.

The graphical user interface is also operable to display assets 11 whichhave drifted from the location to which the assets 11 are assigned inthe asset drift report 117, an example of which is shown in FIG. 17. Theasset drift report identifies which workstations have a reportinglocation 40 different from the workstation's assigned location 38.Included in the report art actionable items 50 which are a list ofrecommended actions that may help produce a more efficiently allocatedfleet 13 of assets 11. For example, the Asset Drift Report may includerecommendations such as, “If an asset is repeatedly on this report afterhaving been moved back to its assigned location, consider reassigningthe Assigned location for the device to that location.”

The graphical user interface 32 provides a user access to dynamicallyupdated information, calculations, predictions, and suggestions of afleet 13 of assets 11 that is powered by the AMS 10. The graphical userinterface provides up-to-date and predictive snapshots of a variety ofproblems identified in the prior art pertaining to fleet and assetmanagement and allocation for an efficient and effective fleet.

Thus, although there have been described particular embodiments of thepresent invention of a new and useful BATTERY AND WORKSTATION MONITORINGSYSTEM AND DISPLAY, it is not intended that such references be construedas limitations upon the scope of this invention.

What is claimed is:
 1. A mobile workstation comprising: a processor; a memory storage device operably coupled to the processor; a transceiver operably coupled to the processor for sending and receiving data via a wireless network; and a sensor operable to send data to the processor when a specified event occurs relating to the mobile workstation, wherein the data is processed by the processor to determine use states of the mobile workstation based on the specified event, wherein the sensor further comprises a back current draw sensor, wherein the use state of the mobile workstation is updated to in-use when the back current draw sensor detects a threshold level of back current draw by the mobile workstation, and wherein the processor is operable to update the threshold level of back current draw by the mobile workstation when an accessory is operably coupled to the mobile workstation.
 2. The mobile workstation of claim 1, wherein the sensor further comprises an accelerometer, and wherein the use state of the mobile workstation is updated to in-transit when the accelerometer detects acceleration of the mobile workstation.
 3. The mobile workstation of claim 1, wherein the sensor further comprises an NFC reader, and wherein the use state of the mobile workstation is updated to at-the-ready when a user having an authorized NFC tag is within operational proximity of the NFC reader.
 4. The mobile workstation of claim 1, wherein the transceiver is operably coupled to a remote server via the wireless network, and wherein the server is operable to determine periods of use for the mobile workstation based on the use states of the mobile workstation.
 5. The mobile workstation of claim 4, wherein the server is operable to determine high-use periods for a fleet of mobile workstations based on the use states of the mobile workstation and other workstations of the fleet of mobile workstations.
 6. A non-transitory computer readable medium having stored therein instructions, that when executed by a computing device, cause the computing device to perform functions comprising: receiving a data packet from a sensor residing on a mobile workstation, the data packet representing an occurrence of a specified event relating to a use of the mobile workstation and detected by the sensor; assigning a use state to the mobile workstation based on the data packet received from the data packet received from the sensor; determining, over a predefined timeline, use states of a fleet of mobile workstations; determining high-use periods for the fleet of mobile workstations; and assigning a status of high return-on-investment to the workstation when the workstation is determined to be in-use during a threshold number of high-use periods.
 7. The non-transitory computer readable medium of claim 6, further comprising generating a heat map of a return-on-investment level for each mobile workstation of the fleet of mobile workstations.
 8. The non-transitory computer readable medium of claim 6, further comprising providing dynamic recommendations for reallocating the mobile workstation within the fleet of mobile workstations based on the use state of the mobile workstation during high-use periods.
 9. The non-transitory computer readable medium of claim 6, wherein the sensor further comprises an accelerometer, and wherein the use state of the mobile workstation is updated to in-transit when the accelerometer detects acceleration of the mobile workstation.
 10. The non-transitory computer readable medium of claim 6, wherein the sensor further comprises a back current draw sensor, wherein the use state of the mobile workstation is updated to in-use when the back current draw sensor detects a threshold level of back current draw by the mobile workstation.
 11. The non-transitory computer readable medium of claim 6, wherein the sensor further comprises an NFC reader, and wherein the use state of the mobile workstation is updated to at-the-ready when a user having an authorized NFC tag is within operational proximity of the NFC reader.
 12. A method of managing a fleet of mobile workstations in a facility, comprising: sensing, by a sensor residing on a mobile workstation of the fleet of mobile workstations, an occurrence of a specified event relating to the use of the mobile workstation; transmitting, by a transceiver resident on the mobile workstation and operably coupled to the sensor, to a remote server a data packet, the data packet representing the occurrence of the specified event; assigning, by the server, a use state to the mobile workstation based on the data packet received by the server; determining, by the server, over a predefined timeline, use states for each mobile workstation of the fleet of mobile workstations; determining, by the server, high-use periods for the fleet of mobile workstations; and assigning a status of high return-on-investment to the mobile workstation when the mobile workstation is determined to be in-use during a threshold number of high-use periods.
 13. The method of claim 12, further comprising generating a heat map of a return-on-investment level for each mobile workstation of the fleet of mobile workstations.
 14. The method of claim 12, further comprising providing dynamic recommendations for reallocating the mobile workstation within the fleet of mobile workstations based on the use state of the mobile workstation during high-use periods.
 15. The method of claim 12, wherein the sensor further comprises an accelerometer, and wherein the use state of the mobile workstation is updated to in-transit when the accelerometer detects acceleration of the mobile workstation.
 16. The method of claim 12, wherein the sensor further comprises a back current draw sensor, wherein the use state of the mobile workstation is updated to in-use when the back current draw sensor detects a threshold level of back current draw by the mobile workstation. 